This code imports, cleans, and merges MODIS product data exported using the AppEEARS web application.

Exported MODIS data for the period 2000-2016:

Data was exported for 50 BAMS paper sites (.csv format, “ID”, “Category”, “LAT”, “LONG”)


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data.table 1.12.0  Latest news: r-datatable.com

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9 MODIS products are downloaded in 5 .csv files:

[1] "./CH4-V2-0-MOD09A1-006-results.csv"  "./CH4-V2-0-MOD11A2-006-results.csv"  "./CH4-V2-0-MOD13Q1-006-results.csv" 
[4] "./CH4-V2-0-MOD15A2H-006-results.csv"
[[1]]

[[2]]

[[3]]

[[4]]
NA

Simplify data by removing unnecssary fields:

$vis

$lst_day

$evi

$lai

$lst_night
NA

Look at what the data quality levels are, convert good to 1, keep only 1s

$vis

$lst_day

$evi

$lai

$lst_night
NA

Make a plot of daytime LST to make sure things look OK

Create a dataframe for each product consisting of LAT, LONG, DATE and the product field (e.g. LSTD)

Create a full dataframe with each site name/latlong repeated n = length(2001-01-01 to 2014-12-31) (FLUXCOM data window). Then right_join each modis product to the site name/all dates tibble to produce a complete dataframe of modis data.

full.date <- as_tibble(rep(as_date(c(as_date("2001-01-01"):as_date("2014-12-31"))),59)) ## rep by number of sites 
names(full.date) <- "DATE"
full.date <- full.date %>% add_column(ID = rep(bams.sites$ID, each = length(full.date$DATE)/59)) %>% 
                              add_column(LAT = rep(bams.sites$LAT, each = length(full.date$DATE)/59)) %>%
                                add_column(LONG = rep(bams.sites$LONG, each = length(full.date$DATE)/59))
modis.full <- full_join(full.date, lst_d.filtered, by =c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lst_n.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, evi.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, b02.filtered, by = c("DATE",'LAT',"LONG"))
modis.full <- full_join(modis.full, b05.filtered, by = c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lai.filtered, by=c("DATE","LAT","LONG"))
modis.full <- modis.full %>% distinct(ID, DATE, .keep_all = TRUE)
str(modis.full)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   301669 obs. of  10 variables:
 $ DATE: Date, format: "2001-01-01" "2001-01-02" "2001-01-03" "2001-01-04" ...
 $ ID  : Factor w/ 59 levels "ATNeu.csv","BCBog.csv",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ LAT : num  47.1 47.1 47.1 47.1 47.1 ...
 $ LONG: num  11.3 11.3 11.3 11.3 11.3 ...
 $ LSTD: num  NA NA NA NA NA NA NA NA NA NA ...
 $ LSTN: num  NA NA NA NA NA ...
 $ EVI : num  NA NA NA NA NA NA NA NA NA NA ...
 $ b02 : num  0.198 NA NA NA NA ...
 $ b05 : num  0.169 NA NA NA NA ...
 $ LAI : num  NA NA NA NA NA NA NA NA NA NA ...

Finished! Save as .csv

# set wd
setwd("C:/Users/gavin/Box/MODIS Data/AppEEARS Data")
write.csv(modis.full, "V2.0_MODIS.csv")
---
title: "Prepare MODIS Data V2.0"
output: html_notebook
---

This code imports, cleans, and merges MODIS product data exported using the [AppEEARS](https://lpdaacsvc.cr.usgs.gov/appeears) web application.

Exported MODIS data for the period 2000-2016:

* **MOD09A1:** surf reflectance b02 b05
* **MOD11A2:** LST Day 1km & LST Night 1km (8 day)
* **MOD13Q1:** EVI 250m (16 day)
* **MOD15A2H:** fPAR 500m & LAI 500m (8 day)

Data was exported for 50 BAMS paper sites (.csv format, "ID", "Category", "LAT", "LONG")
```{r echo=FALSE, warning=FALSE, message=FALSE}
# clear workspace and get wd
rm(list=ls())
# change wd
setwd("C:/Users/gavin/Box/MODIS Data/AppEEARS Data/")
bams.sites <- read.csv("V2.0_sites.csv")
names(bams.sites)[c(3,4)] <- c("LAT", "LONG")
bams.sites
```


```{r echo=FALSE}
# libraries
# load tidyverse libraries
library(dplyr)
library(tidyr)
library(readr)
library(readxl)
library(stringr)
library(lubridate)
library(RColorBrewer)
library(ggplot2)
library(data.table)
library(magrittr)
library(caret)
library(caTools)
library(ranger)
library(tibble)
```

9 MODIS products are downloaded in 5 .csv files:
```{r message=FALSE, warning=FALSE, echo=FALSE}
# change wd
setwd("C:/Users/gavin/Box/MODIS Data/AppEEARS Data/ch4-v20")
# get site names from directory
site.names <- list.files(pattern = "\\.csv$",full.names=TRUE)
site.names
  # create list of products
modis <- list()
# create a function to read many csvs
read.many.csv <- function(i){
  read.csv(i, header=TRUE)
}
# execute function with site.names
modis <- lapply(site.names, read.many.csv)
modis
```
Simplify data by removing unnecssary fields:
```{r echo = FALSE}
vis.names <- names(modis[[1]][c(3,4,5,9,10,14)])  #09A1 surface ref b02 b05
lst.names <- names(modis[[2]][c(3,4,5,9,10,15,24)]) ##11A2 LSTDN
evi.names <- names(modis[[3]][c(3,4,5,9,13)]) ##13Q1 EVI
lai.names <- names(modis[[4]][c(3,4,5,10,15)]) ## 5A2H
names.vec <- list(vis.names,lst.names, evi.names, lai.names)

modis.simple <- list()
for (i in 1:length(modis)){
     modis.simple[[i]] <- as_tibble(modis[[i]][names.vec[[i]]])
     modis.simple[[i]]$Date <- as_date(modis.simple[[i]]$Date)
}
#split night and day LST; evi and NDVI; LAI and fPAR.
modis.simple[[5]] <- modis.simple[[2]] %>% select(Latitude, Longitude, Date, MOD11A2_006_LST_Night_1km, 
                                                  MOD11A2_006_QC_Night_MODLAND_Description)
modis.simple[[2]] <- modis.simple[[2]] %>% select(Latitude, Longitude, Date, MOD11A2_006_LST_Day_1km,
                                                  MOD11A2_006_QC_Day_MODLAND_Description)
names(modis.simple) <- c("vis","lst_day","evi","lai","lst_night")
modis.simple
```
Look at what the data quality levels are, convert good to 1, keep only 1s
```{r echo= FALSE}
#convert levels to factor
levels(modis.simple[[1]][[6]]) <- as.factor(c(0,1,0))
levels(modis.simple[[2]][[5]]) <- as.factor(c(0,1,0))
levels(modis.simple[[3]][[5]]) <- as.factor(c(0,0,1,0))
levels(modis.simple[[4]][[5]]) <- as.factor(c(1,0))
levels(modis.simple[[5]][[5]]) <- as.factor(c(0,1,0))

#rename variables
names(modis.simple[[1]]) <- c("LAT", "LONG", "DATE", "b02", "b05", "QUALITY")
names(modis.simple[[2]]) <- c("LAT", "LONG", "DATE", "LSTD", "QUALITY")
names(modis.simple[[3]]) <- c("LAT", "LONG", "DATE", "EVI", "QUALITY")
names(modis.simple[[4]]) <- c("LAT", "LONG", "DATE", "LAI", "QUALITY")
names(modis.simple[[5]]) <- c("LAT", "LONG", "DATE", "LSTN", "QUALITY")

# modis.simple
modis.filtered <- list()
for (i in 1:length(modis.simple)){
  modis.filtered[[i]] <- as_tibble(modis.simple[[i]] %>% filter(QUALITY == 1))
}
modis.filtered
names(modis.filtered) <- c("vis","lst_day","evi","lai","lst_night")
```
Make a plot of daytime LST to make sure things look OK
```{r}
ggplot(modis.filtered[[2]], aes(DATE, LSTD, col = as.factor(LAT))) +
  geom_point()
```
Create a dataframe for each product consisting of LAT, LONG, DATE and the product field (e.g. LSTD)
```{r}
lst_d.filtered <- modis.filtered$lst_day %>% mutate(LSTD = LSTD) %>% select(LAT, LONG, DATE, LSTD)
lst_n.filtered <- modis.filtered$lst_night %>% mutate(LSTN = LSTN) %>% select(LAT, LONG, DATE, LSTN) 
evi.filtered <- modis.filtered$evi %>% mutate(EVI = EVI) %>% select(LAT, LONG, DATE, EVI) 
b02.filtered <- modis.filtered$vis %>% mutate(b02 = b02) %>% select(LAT, LONG, DATE, b02)
b05.filtered <- modis.filtered$vis %>% mutate(b05 = b05) %>% select(LAT, LONG, DATE, b05)
lai.filtered <- modis.filtered$lai  %>% mutate(LAI = LAI) %>% select(LAT, LONG, DATE, LAI)

```
Create a full dataframe with each site name/latlong repeated n = length(2001-01-01 to 2014-12-31) (FLUXCOM data window).
Then right_join each modis product to the site name/all dates tibble to produce a complete dataframe of modis data.
```{r}
full.date <- as_tibble(rep(as_date(c(as_date("2001-01-01"):as_date("2014-12-31"))),59)) ## rep by number of sites 
names(full.date) <- "DATE"
full.date <- full.date %>% add_column(ID = rep(bams.sites$ID, each = length(full.date$DATE)/59)) %>% 
                              add_column(LAT = rep(bams.sites$LAT, each = length(full.date$DATE)/59)) %>%
                                add_column(LONG = rep(bams.sites$LONG, each = length(full.date$DATE)/59))

modis.full <- full_join(full.date, lst_d.filtered, by =c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lst_n.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, evi.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, b02.filtered, by = c("DATE",'LAT',"LONG"))
modis.full <- full_join(modis.full, b05.filtered, by = c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lai.filtered, by=c("DATE","LAT","LONG"))

modis.full <- modis.full %>% distinct(ID, DATE, .keep_all = TRUE)
str(modis.full)
```

Finished! Save as .csv
```{r warning=FALSE, message=FALSE}
# set wd
setwd("C:/Users/gavin/Box/MODIS Data/AppEEARS Data")
write.csv(modis.full, "V2.0_MODIS.csv")
```




